RESEARCH Open Access An automatic segmentation and ... · Anti-Nuclear Antibody (ANA) is an autoantibody produced by the immune system directed against the self body tissues or cells

RESEARCH Open Access

An automatic segmentation and classificationframework for anti-nuclear antibody imagesChung-Chuan Cheng1, Tsu-Yi Hsieh2, Jin-Shiuh Taur1*, Yung-Fu Chen3,4,5*

From 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Work-shop on Current Challenging Image Analysis and Information Processing in Life SciencesOsaka, Japan. 3-7 July 2013

* Correspondence: [email protected]; [email protected] of ElectricalEngineering, National Chung HsingUniversity, Taichung, Republic ofChina: Taiwan3Department of HealthcareAdministration, Central TaiwanUniversity of Science andTechnology, Taichung, Republic ofChina: Taiwan

Abstract

Autoimmune disease is a disorder of immune system due to the over-reaction oflymphocytes against one’s own body tissues. Anti-Nuclear Antibody (ANA) is anautoantibody produced by the immune system directed against the self body tissuesor cells, which plays an important role in the diagnosis of autoimmune diseases.Indirect ImmunoFluorescence (IIF) method with HEp-2 cells provides the majorscreening method to detect ANA for the diagnosis of autoimmune diseases.Fluorescence patterns at present are usually examined laboriously by experiencedphysicians through manually inspecting the slides with the help of a microscope,which usually suffers from inter-observer variability that limits its reproducibility.Previous researches only provided simple segmentation methods and criterions forcell segmentation and recognition, but a fully automatic framework for thesegmentation and recognition of HEp-2 cells had never been reported before. Thisstudy proposes a method based on the watershed algorithm to automatically detectthe HEp-2 cells with different patterns. The experimental results show that thesegmentation performance of the proposed method is satisfactory when evaluatedwith percent volume overlap (PVO: 89%). The classification performance using a SVMclassifier designed based on the features calculated from the segmented cellsachieves an average accuracy of 96.90%, which outperforms other methodspresented in previous studies. The proposed method can be used to develop acomputer-aided system to assist the physicians in the diagnosis of auto-immunediseases.

IntroductionThe immune system enables us to resist infections by counteracting invading organisms.

Autoimmune disease is a disorder of immune system due to over-reaction of lympho-

cytes against one’s own body tissues [1]. Common autoimmune diseases include

Hashimoto’s thyroiditis, rheumatoid arthritis, diabetes mellitus type 1, and lupus

erythematosus. Anti-Nuclear Antibody (ANA) is an autoantibody produced by the

immune system directed against the self body tissues or cells. The ANA test widely

used to detect antibody in the blood plays an important role in the diagnosis of auto-

immune diseases. When a particular antibody pattern has been detected, the patient

may have the possibility of acquiring certain autoimmune diseases.

Cheng et al. BioMedical Engineering OnLine 2013, 12(Suppl 1):S5http://www.biomedical-engineering-online.com/content/12/S1/S5

© 2013 Cheng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Indirect ImmunoFluorescence (IIF) technique applied on HEp-2 cell substrates

provides the major screening method to detect ANA patterns in the diagnosis of auto-

immune diseases. It produces the ANA images with distinct fluorescence intensities

and staining patterns through IIF slides. Currently, the ANA patterns are inspected by

experienced physicians to identify abnormal cell patterns, which is a laborious task and

may cause harm to physicians’ eyes. It is not easy to train a qualified physician in a

short term. Furthermore, manual inspection suffers from the difficulties, such as intra-

and inter-observer variability, that limit the reproducibility of IIF readings [2-5].

Although previous studies have proposed several methods for automatic segmenta-

tion of ANA cells [6,7] and criteria for recognition of cell patterns [3,6,8-10], a fully

automatic segmentation and recognition framework has never been developed so far.

In this study, we propose a framework based on the watershed approaches to automa-

tically segment the HEp-2 cells. It is a crucial preprocessing step for a computer aided

system to classify the cell patterns to provide information to assist physicians in disease

diagnosis and treatment.

Since the cytoplasm of HEp-2 cells is invisible in the IIF images, in what follows, the

term “cell” means cell nucleus, “foreground” indicates the cell region, and “background”

denotes the rest of the image. The rest of this paper is organized as follows. Section

“Related Works” reviews the techniques used for ANA image segmentation and cell

recognition in previous studies. Section “Segmentation of ANA Cells” describes the

methods proposed in this study for the segmentation of ANA cells. Classification of

ANA cell patterns is demonstrated in section “Cell Classification of ANA Images”.

Finally, discussions, conclusions, and future works are made in sections “Discussion” and

“Conclusion and Future Work”.

Related worksIn this section, the methods proposed in previous investigations for the segmentation

and classification of ANA cell images are presented.

ANA image segmentation

Perner et al. [6] used image processing techniques, including image transformation, his-

togram equalization, Otsu thresholding [11], and morphological operation, to obtain a

binary mask for segmenting the cells from the ANA images. By modifying the methods,

Huang et al. [7] presented two adaptive automatic segmentation frameworks to precisely

extract the ANA cells. In their studies, the first framework classified an image into two

categories, i.e., sparse and mass cell regions, based on the number of connected regions.

Depending on the category of the images, different color spaces and processing techni-

ques were adopted for cell segmentation. Morphological operations were also used to

obtain smooth segmentation results. It was demonstrated to be able to deal with the seg-

mentation of different patterns of IIF images. On the other hand, in the second frame-

work, watershed segmentation [12] was applied on the green channel of the RGB

images, followed by region merging and elimination to obtain the cell boundaries. If the

number of regions in the obtained image was larger than a pre-defined threshold, the

framework converted the original image into CMY color space and performed marker-

controlled watershed segmentation [13] on the cyan color component. It was reported

that the segmentation performance achieved an overall sensitivity of 94.7%.


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Creemers et al. [14] proposed a unsupervised segmentation algorithm, based on

iterative global Otsu thresholding and morphological opening operation, to support IIF

testing. It was reported to have the capability to split connected regions into individual

regions with an average accuracy of 89.57%.

ANA cell recognition

Perner [8] presented the first study on fluorescent image analysis, feature extraction

and classification. Then, an automatic cell recognition approach based on a variety of

features, including size, color density, and number of cells, extracted from the segmen-

ted images was proposed [6]. For the cells with identical color density, features includ-

ing standard deviation, mean shape factor, mean of perimeter, and standard deviation

of perimeter, were further extracted. Data mining techniques, including Boolean model

and decision tree induction, were then used to label the cell regions. Finally, human

experts tagged each labeled region with a semantic label. Based on the aforementioned

methods, Sack et al. [3] presented a system to automatic classify HEp-2 fluorescent

patterns with a classification accuracy greater than 83%.

According to the fluorescence intensity, Soda and Iannello [9] classified the ANA

images into a variety of patterns. They further proposed a framework consisting of

hybrid rule-based multi-expert systems for the classification of ANA patterns with an

overall error rate of 2.7-5.8% [15]. The framework extracted the features including the

first, second, and fourth moments of the gray-level co-occurrence matrix, Zernike

moments, as well as the coefficients of discrete cosine transform (DCT) and discrete

wavelet transform (DWT). Based on the efforts of previous researches, Rigon et al.

[16] proposed a comprehensive system based on two approaches, in which the first

approach discriminated the positive cells from the negative and weakly positive cells

based on the features of fluorescence intensity, whereas the second one recognized the

staining pattern of the positive cells. The performance of positive/negative recognition

ranges from 87% to more than 94%, whereas the staining pattern classification accu-

racy of the main classes, i.e. homogeneous cells, peripheral nuclear cells, speckled cells,

nucleolar cells, and artefacts, ranges from 71% to 74%.

Elbischger et al. [17] developed an iterative thresholding algorithm for processing

HEp-2 cells and a cell classifier for detecting auto-immune diseases. Features including

area to perimeter ratio, variance, 30th and 60th normalized percentiles, percentile

range, dent number, auto-covariance percentage, and roundness, were extracted from

the segmented cells and used for cell classification. The system was reported to be cap-

able of distinguishing 5 different patterns with an overall accuracy of 93% based on the

dataset consisting of 982 ROIs extracted from 38 images.

Recently, Huang et al. [18] employed the self-organizing map (SOM) to identify the

fluorescence patterns of HEp-2 cells. Fourteen features, including the perimeter, area,

and histogram uniformity of the cell; area and average intensity of the inside and peri-

meter areas of the cell; higher and lower intensity ratios of the inside area, perimeter

area, and whole area of the cell; and standard deviation of the inside area of the cell,

were used for designing the classifier with an average accuracy of 92.4%. In [19], the

EUROPattern designed based on k-nearest neighbor algorithm, was compared with the

conventional visual IIF evaluation with the sensitivity and specificity achieving 100%

and 97.5%, respectively. In addition, it was shown that 94.0% of all the main antibody


Page 3 of 25

patterns, including the positive patterns, i.e., homogenous, speckled, nucleolar, centro-

mere, nuclear dotted, and cytoplasmic patterns, as well as the negative patterns, could

be correctly recognized.

Segmentation of ANA cellsAs recommended by Center for Disease Control (CDC) [20,21], in this study, the IIF

slides were prepared at 1:80 serum dilution, and the ANA images were acquired by a

digital camera mounted on a fluorescence microscope at a 40-fold zoom. The images

were stored with the format of 24-bit RGB color depth and a resolution of 3136×2352

pixels. As shown in Figure 1, the ANA cells are classified into six categories: diffused,

peripheral, nucleolar, coarse-speckled, fine-speckled, and discrete-speckled patterns. A

dataset, consisting of 196 images classified into 37 diffused, 29 peripheral, 5 nucleolar,

94 coarse-speckled, 1 fine-speckled, and 30 discrete-speckled images by an expert (Dr.

Hsieh), was used for the experiments. The procedure of the proposed method is illu-

strated in Figure 2.

Since the original images are stained by green dye, the proposed method extracts

only the green channel from the original RGB ANA images for processing. In order

to reduce the computation time, the images are downsized from 3136×2352 to

1024×768 pixels. It was found that images at this resolution can still provide enough

information for the segmentation and classification of cell patterns. Figure 3 shows

an example of ANA image and its corresponding green-channel. As described in the

following 3 subsections, the proposed segmentation method divided into 3 proce-

dures, i.e. pre-classification, cell detection 1, and cell detection 2, is presented. The

parameters used for these 3 procedures are described in subsection “Parameters for

cell segmentation”. Finally, the segmentation results are demonstrated in subsection

“Segmentation results”.

Figure 1 Example of ANA images. Images classified into (a) diffused, (b) peripheral, (c) nucleolar, (d)coarse-speckled, (e) fine-speckled, and (f) discrete-speckled patterns.


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Pre-classification

Automatic segmentation of ANA images cannot be handled in a unified way because the

characteristics of the images in different categories are quite dissimilar. For example, the

discrete speckled cells look like irregular broken blobs and are significantly different

from the cells of other 5 categories that appear as elliptic blobs but still having diverse

appearances (cf. Figure 1). Thus, the images are pre-classified according to their differ-

ences in image patterns before conducting cell segmentation. In the pre-classification

stage, the images are divided into two groups. Images with larger grey-level variance or

more regions contained in the foreground are assigned to the first group, and the rest of

the images are assigned to the second group. The images in these two groups are seg-

mented using different methods as detailed in subsections “Cell detection 1“ and “Cell

detection 2“. The procedure of pre-classification is summarized as follows:

1) First, Otsu thresholding algorithm is used to roughly separate the foreground

regions from the background.

2) The closing morphological operation is employed to fill the holes and to eliminate

small regions in the foreground.

3) If the number of foreground regions in an image is larger than the threshold,

th_num, or its foreground regions contain staining noises with variance higher than

the threshold, th_fg_var, it is segmented using “Cell detection 1"; otherwise “Cell

Figure 2 Procedure of the proposed method.

Figure 3 Green-channel image. Example of (a) an original ANA image and (b) its green-channel image.


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detection 2” is adopted. In this study, the thresholds th_num and th_fg_var are set to

200 and 1000, respectively.

4) For images segmented with “Cell detection 2”, the staining noise in the back-

ground regions are removed according to their noise level, as defined in the following

equation:

128∑

i=0

[p (i) > 0

],

where i indicates the gray level of the image, and p(i) denotes the frequency of gray

level i in the image. The threshold of noise level, th_noise, is set to 10.

Cell detection 1

The approach is designed for cell detection of the images containing more foreground

regions or the gray level in the foreground regions presenting great variance. It consists

of two stages: image segmentation and cell extraction. Cells are extracted according to

the cell contours obtained from the general watershed segmentation [12] and marker-

controlled watershed segmentation [13]. As described below, the procedure of this

approach is divided into four steps. Figure 4 illustrates the results obtained from indivi-

dual steps.

1) The histogram equalization is applied to the original image in Figure 4(a), and

then the pixels with gray level greater than 240 are considered as the initial markers,

as presented in Figure 4(b).

2) As demonstrated in Figure 4(c), the original image is smoothed by the morpholo-

gical opening operation using a disk-shaped structuring element with a radius of 15.

3) The Difference between the original image and the smoothed image is obtained,

Figure 4(d), and is converted into a binary image, Figure 4(e), by applying the Otsu

thresholding method.

4) The initial markers are superimposed on the thresholded image shown in Figure 4

(e), followed by applying the same opening morphological operation mentioned in the

2nd step to obtain the marker image, Figure 4(f), used for marker-controlled watershed

segmentation. The flowchart of marker extraction is depicted in Figure 5.

As described in Steps 5-7, 3 types of watershed images are obtained based on the

original image, smooth image, and marker image, and are used for cell segmentation.

5) The original image shown in Figure 4(a) is complemented by subtracting each

pixel value from 255 for conducting watershed segmentation. Figure 6(a) shows the

background watershed image (b-ws) superimposed on the original image.

6) Furthermore, Gaussian differentiation parameter with s=2 and thresholding parameter

of h-minima suppression with th_h1 = 0.12 [22] are applied on the smoothed image for

conducting watershed segmentation to obtain the foreground watershed segmentation

image (f-ws). Figure 6(b) presents the “f-ws” image superimposed on the original image.

7) Similar to the foreground watershed segmentation, the smoothed image is first fil-

tered by the Gaussian differentiation and minima suppressed by h-minima transform,

which is then superimposed by the marker image and the “b-ws” image to obtain the fore-

ground marker-controlled watershed (fmc-ws) image used for marker-controlled

watershed segmentation. Figure 6(c) shows the foreground marker-controlled watershed

(fmc-ws) image.


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Figure 4 Extraction of marker images. (a) The original, (b) initial marker, and (c) smooth images. (d)Difference image obtained from the original image and the smooth image. (e) Image after performing Otsuthresholding on difference image and (f) the marker image after applying opening morphological operation.

Figure 5 Flowchart of marker extraction.


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These 3 types of watershed images were further used for cell segmentation.

As demonstrated in Figure 6, it can be observed that the “b-ws” image is effective in

splitting cells that are close to each other. The blobs in “fmc-ws” are mostly over-

segmented with unsmooth contours, resulting in a failure to effectively delineate the

cell contours. On the other hand, the “f-ws” image is unable to detect some of the

cell regions. Consequently, in the cell extraction stage of “Cell detection 1”, the three

types of watershed images and the marker image are combined to precisely extract

cell boundaries.

As illustrated in Figure 7, the strategies for cell extraction using the watershed

images are described in the following steps:

1) The three watershed images, i.e., “b-ws”, “f-ws” and “fmc-ws”, are all binary

images. The cell contours in “f-ws” and “fmc-ws” images are labeled as ZERO, other-

wise ONE, followed by the removal of background regions to obtain the watershed

mask images shown in Figure 8(a) and 8(b), respectively.

2) The cell regions are extracted from the “fmc-ws” image according to the peri-

meters of the connected regions, since it can potentially detect more cell regions than

“f-ws”. The regions whose areas larger than the threshold th_area are justified by

“ellipse test” and considered as the cells after having passed the test.

3) For the regions with areas smaller than the threshold th_area, closing morpholo-

gical operation is conducted to merge smaller regions. The merged regions are then

justified by ellipse test for cell extraction. As demonstrated in Figure 9, the small inner

regions of the remains are merged to larger regions.

Figure 6 Examples of three types of watershed images. (a) Background-watershed (b-ws) image, (b)foreground-watershed (f-ws) image, and (c) foreground marker-controlled-watershed (fmc-ws) imagesuperimposed on the original image.


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4) For the regions, which are not deemed as ellipses from the remains of the “fmc-ws”

at Step 3 and the “f-ws”, having areas higher than th_area and containing markers in

the corresponding locations, they should be treated as the candidate cells. Due to the

fact that the blobs of “f-ws” are more similar to real cells than “fmc-ws”, “f-ws” is used

to perform cell extractions before applying “fmc-ws” here.

5) Most of the cells in “f-ws” and “fmc-ws” should have been extracted at the pre-

vious 4 steps, but some regions may not be detected because their markers are large

enough to cover the edges of the regions. Figure 10(a) demonstrates the cells detected

at Steps 1-4. However, as shown in Figure 10(b), watershed segmentation may fail

when detecting the cells whose corresponding markers are large enough to cover the

whole candidate cell. Hence, if the markers existed in the marker-image are larger

than the threshold th_area2, the watershed segmentation (with a parameter of

h-minima transform th_h2) is performed in the corresponding region of the smooth

image. Here, only the corresponding region of “b-ws”, as shown in Figure 10 (c), is

considered for extracting the cells.

Due to the fact that the real HEp-2 cells usually appear as ellipses, the cells can be

justified by “ellipse test”. It is used to justify whether a region contains a cell or not.

Given a region ri, the error between ri and a real ellipse riI is defined as:

ei =|ri ′||ri| , with ri ′ = ri XOR rIi and rIi = Ellipse(a, b, θ)

Figure 7 Procedure of cell extraction in “Cell detection 1”.


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in which |ri| denotes the number of pixels in ri and riI is the estimated ideal ellipse

for ri, comprising three parameters: major-length (a), minor-length (b), and orientation

(θ). The lengths of major axis and minor axis are both computed according to the cen-

troid of ri. If the error function of a region is equal to zero, the region is deemed as an

ideal ellipse. Figure 11 depicts a region and its estimated ideal ellipse. If the error of a

region is lower than the threshold th_error, it is marked as a cell; otherwise, it may be

treated as one of following cases: not a cell, an incomplete cell, or a connected region.

Cell detection 2

The approach is applied to the images containing less foreground regions

(th_num<200) and less staining noise (th_fg_var<1000) detected in the foreground. As

shown in Figure 12, the procedure is very similar to that in “Cell detection 1”, except

that the image segmentation uses only the “b-ws” (red) and “f-ws” (green) watershed

images without considering the “fmc-ws” image. The procedure is described as follows:

1) Remove the background regions of the “f-ws” image.

2) Extract cell regions from “f-ws”.

3) Because of the characteristics of watershed segmentation, the adjacent regions

form connected regions. The regions which are not extracted in step 2 may be fake

Figure 9 Merging the small inner regions of a region not justified as a cell. (a) Inner regions whichare not justified as cells and (b) larger inner regions obtained by merging smaller inner regions withmorphological closing operation.

Figure 8 Mask images of cell regions with background removal. (a) “f-ws” image and (b) “fmc-ws”image.


Page 10 of 25

connected regions, which can be split by using the information embedded in the “b-ws”

image. As illustrated in Figure 13(a), a sub-region, which connects two watershed

regions, with a line in the “b-ws” image crossing it will be eliminated, resulting in the

separation of two cell blobs, Figure 13(b). Subsequently, watershed segmentation (with a

designated parameter of h-minima transform, th_h1) is further performed on the

Figure 10 Extracting cells for regions with large markers. Illustration of (a) 8 detected cells (green) and1 undetected cell, (b) the marker corresponding to the b-ws region containing the undetected cell, and (c)superimposition of images shown in (a) and (b).

Figure 11 A region ri and its fitted ellipse riI(green).


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individual cell regions appeared on “f-ws”, Figure 13 (c). The sub-regions in the refined

cell regions are merged and justified by “ellipse test” afterward.

4) For connected cell regions which can’t be split at step 3, all possible combinations

of sub-regions will be tested to obtain combinations of sub-regions which are similar

to ellipses. Once the best combination has been obtained, the cell regions can be well-

separated from the background. Figure 14 illustrates the procedure in splitting the

region containing three candidate cell regions. A connected region ri consisting of Ni

sub-regions can be indicated as:

ri = {ri,1, ri,2, · · · ri,j, · · · ri,Ni }

The error function of the kth combination of sub-regions, combk, can be calculated

according to:

ek =

∣∣comb′k

∣∣|combk| ,

where comb′k = combk XOR combIk with combIk denoting the estimated ideal ellipse of

combk. If the combination with smallest error, k′ = argmin

kek, has been found, the con-

nected region can be split to isolated regions ri’ accordingly. As shown in Figure 14(c),

“b-ws” image is superimposed on the “f-ws” image to form 17 sub-regions. The

Figure 13 Splitting of a fake sub-region. (a) A fake sub-region with a line in “b-ws” crossing it is split to(b) two separated candidate cell regions. (c) Subsequent watershed segmentation is performed on thecandidate cell regions on “f-ws”.

Figure 12 An example of cell segmentation using “Cell Detection 2”.


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combinations with smallest errors include {ri,1, ri,2, ri,5, ri,9}, {ri,4, ri,10, ri,11}, {ri,14, ri,15,

ri,16, ri,17}, and {ri,3, ri,6, ri,7, ri,8, ri,12}. After the ellipse tests, the sub-region combina-

tions {1, 2, 5, 9}, {4, 10, 11}, and {14, 15, 16, 17} are merged into 3 cell regions, while

the combination {3, 6, 7, 8, 12} is discarded. Once a connected region has been split,

the new regions are modified by performing watershed segmentation (with a desig-

nated parameter of h-minima transform, th_h2) in their locations corresponding to the

locations in the “b-ws” image. The sub-regions {3, 6, 7, 8, 12} are discarded because

their intensities and textures are very similar to the background when the local

watershed segmentation has been applied.

5) Due to the fact that the foreground of dataset images may contain inhomogeneous

gray levels, some regions can not be detected because they are darker than other regions,

even though they can be discriminated by human eyes. In order to detect these regions,

global Otsu thresholding is again performed on the remaining image after cell extrac-

tion. Detected regions with areas greater than th_area are considered as cells.

Parameters for cell segmentation

The parameters used for different stages of cell segmentation are listed in Table 1. The

parameters th_h1 and th_h2 are crucial in effectively suppressing noises and local irre-

gularities in the gradient images. Furthermore, the segmentation results are very sensi-

tive to the parameters, even they are only changed slightly; hence they are set case by

case for obtaining complete blobs and avoiding over-segmentation. If the values are

too small, the blob will be over-segmented and need more time to find ri’, which is the

combination of sub-regions with the smallest error. In contrast, larger values may

cause the watershed to reach a boundary outside the blob and cannot converge at the

real boundaries. The procedures of setting the parameters th_h1 and th_h2 are based

on the greedy algorithm.

Figure 14 Example of splitting a connected region. (a) A connected region is split into 3 cell regionsby (b) superimposing “b-ws” image on “f-ws” image and then (c) determining combined sub-regions anddiscarded sub-regions.

Table 1 Parameters designated for different stages of cell segmentation.

Pre-classification Cell Detection 1 Cell Detection 2

Parameter Value Parameter Value Parameter Value

th_num 200 th_h1 0.12 th_h1 0.12

th_noise 10 th_h2 0.28 th_h2 0.28

th_fg_var 1000 th_area 400 th_area 400

th_error 0.095 th_error 0.095

th_area2 32


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The parameters, th_area and th_error, are used as the criteria for judging whether a

blob is a cell or not. Considering an ANA image with a size of 1024×768 pixels, the

minimum cell size is set to 400 pixels, i.e. th_area = 400, according to the physician’s

opinion. Figure 15 compares the errors among regions with different shapes. Note that

a perfect ellipse has a zero error. Since HEp-2 cells may be squeezed, superimposed,

demolished, or deviated from a perfect ellipse, the value of th_error (0.095) is deter-

mined by greedy algorithm with a grid size of 0.005 to select the optimal threshold

with best detecting accuracy according to the 3830 ground-truth cell images extracted

from 196 images in the dataset. On the other hand, the parameter th_area2 is used to

find the markers located in non-recognized cell regions. In the cases of nucleolar and

discrete-speckled patterns, the markers could be too small to be used for cell detection.

Hence, its value is assigned as th_area2 = 32 for mild restriction.

Segmentation results

Figure 16 demonstrates the segmentation results of ANA images with 6 different pat-

terns. As shown in this figure, the proposed method performs well on almost all the

images with different cell patterns; however, the performance on images of diffuse and

discrete-speckled patterns is less satisfactory because the cells of diffused pattern con-

tain more closely connected regions than the other types of cells, whereas the cells of

discrete-speckled pattern appear to have less obvious boundaries. Figure 17 compares

the segmentation results using the proposed method with examples of the ground-

truth images. The ground-truth images were delineated by the technicians trained by

one of the authors, Dr. Hsieh. Performance of the segmentation results was evaluated

Figure 15 Comparisons of th_error values among regions with different shapes.


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with percent volume overlap (PVO) and percent volume difference (PVD) that had

been used widely in previous works [23-26].

Given two contours, denoted by Cs and Cg, obtained from the proposed method and

the ground truth, respectively, of the segmented image, PVO and PVD can be calcu-

lated from the following formula:

PVO(Cs,Cg

)=

V(Cs ∩ Cg

)[V (Cs) + V

(Cg

)]/2

× 100%, and

PVD(Cs,Cg

)=

∣∣V (Cs) − V(Cg

)∣∣[V (Cs) + V

(Cg

)]/2

× 100%,

Figure 16 Examples of segmentation results of ANA images. Segmented cells of images with (a) coarse-speckled, (b) diffused, (c) discrete-speckled, (d) fine-speckled, (e) nucleolar, and (f) peripheral patterns, respectively.


Page 15 of 25

where V(C) indicates the volume of a contour. Table 2 presents the average perfor-

mance of the segmentation results evaluated based on PVO and PVD. The results

show that the proposed method can detect cells accurately in most image cases with

the PVO greater than 89% and the PVD less than 22%. Even for the most difficult

Figure 17 Comparisons of segmentation results between proposed method and ground-truth.Segmented results of images with (a) coarse-speckled, (b) diffused, (c) discrete-speckled, (d) fine-speckled,(e) nucleolar, and (f) peripheral patterns overlapped on ground-truth images.

Table 2 Comparisons of cell segmentation performance

Pattern Cell Number PVO (%) PVD (%)

Coarse speckled 260 92.35 15.31

Diffuse 157 87.89 24.23

Discrete speckled 195 78.03 43.95

Fine speckled 67 91.27 17.46

Nucleolar 175 91.94 16.12

Peripheral 153 93.47 13.06

Performance evaluated based on average percent volume overlap (PVO) and percent volume difference (PVD).


Page 16 of 25

cases appear in cells with discrete-speckled pattern, PVO can still achieve a value over

75%. As a matter of fact, it is not necessary to segment HEp-2 cells with great accu-

racy. However, the segmentation results must be good enough to extract features to

support accurate cell classification, as described in next section.

Cell classification of ANA imagesBy considering the effect of astigmatism, the texture details of the cells which are not

located at the central field may be lost due to optical aberration. Hence, only the cells

located in the central field, accounting to 50% of the area from the image center, are

used for cell classification. A total of 3830 cells extracted from 196 images are classi-

fied into 6 different patterns, i.e. diffused (599), peripheral (529), nucleolar (94),

coarse-speckled (1956), fine-speckled (56), and discrete-speckled (596), by an experi-

enced physician, Dr. Hsieh. The classified cell patterns are adopted as the ground truth

to verify classification performance of the proposed method.

Features for cell classification

For the purpose of finding suitable features to represent the patterns of ANA images,

conventional and the state-of-the-art features are investigated. The conventional fea-

tures used to describe patterns include statistics of intensity and texture of blobs. The

statistics of blob intensity include mean, variance, skewness, and entropy. Tamura fea-

tures, including coarseness, contrast, and directionality, as well as the Haralick features,

including contrast, correlation, energy, and homogeneity, obtained from co-occurrence

matrix (GLCM) with 0, 45, 90, and 135 degrees, are also used to characterize the

blobs. Furthermore, the most frequently used state-of-the-art features, such as fuzzy

texture spectrum (FTS) [27,28] and local binary pattern (LBP) [29-31], are also adopted

for cell classification in this study.

In addition, by observing the ANA images, a novel feature has been proposed to

describe the appearance of blobs from the intensity images. As illustrated in Figure 18,

the features obtained from the perimeters and the central areas of the blobs are differ-

ent between two different cell patterns, such as the peripheral and nucleolar patterns.

Figure 18 Intensity difference between perimeter (red) and central area (green).


Page 17 of 25

It can be distinguished by calculating the intensity difference between the perimeter

and the central area of a blob according to the following equation:

dpc = Pavg − Cavg

where Pavg denotes the average intensity of pixels located at the perimeter of a blob,

and Cavg indicates the average intensity of the central area with a size of 7×7 pixels.

By observing images in Figure 1, it can be found that different cell patterns contain a

variety of regions with different sizes and patterns. For example, although nucleolar

and discrete-speckled patterns both contain light regions, the number of light regions

in the cells with discrete-speckled pattern is greater than the nucleolar pattern. In con-

trast, some dark regions can be observed in the coarse-speckled and fine-speckled pat-

terns. These are important and useful characteristics to reduce false cases in

discriminating cells with different patterns. A total of 6 features derived from statistics

of light and dark regions inside the blobs, including numbers of dark and light regions

as well as mean and variance of intensity of dark and light regions, are obtained for

cell discrimination.

In total, 129 candidate features were used to represent the patterns of individual

ANA images. As indicated in Table 3, the features were grouped into 11 categories, i.e.

STATS (3 features), TAMURA (3 features), HARALICK (16 features), FTS (45 fea-

tures), LR (3 features), DR (3 features), LBP8 (10 features), LBP16 (18 features), LBP24

(26 features), ENTROPY (1 feature) and DPC (1 feature).

Design and validation of cell classifierSupport vector machine (SVM) is a supervised learning method widely used for classi-

fication of data patterns [32,33]. A special property of SVM is that it can simulta-

neously minimize the empirical classification error and maximize the geometric margin

of a classifier. It is a powerful methodology for solving problems in nonlinear classifica-

tion, function estimation, and density estimation, leading to many applications [34].

In this study, SVM classifier was implemented by the LIBSVM tool [35] which sup-

ports multi-class classification. Radial basis function (RBF) was selected as the kernel

because of its advantages in mapping samples into a higher dimensional space so that

Table 3 Categories of features used for cell classification.

Category FeaturesNo.

Description

STATS 3 Intensity statistics of blobs: mean, variation, and skewness

TAMURA 3 Coarseness, contrast, and directionality

HARALICK 16 Contrast, correlation, energy, and homogeneity in co-occurrence matrix with degrees0, 45, 90, and 135.

FTS 45 Fuzzy texture spectrum based on the relative intensity levels among pixels

LR 3 Statistics of light regions: No. of regions as well as mean and variation of intensity

DR 3 Statistics of dark regions: No. of regions as well as mean and variation of intensity

LBP8 10 Calculated based on 8 neighbors the distance of one



ENTROPY 1 Intensity statistic of blobs

DPC 1 Intensity difference between perimeter and central area

Total 129


Page 18 of 25

it can handle the case when the relation between class labels and attributes is non-

linear [36]. The optimal combination of penalty parameters, C and g of the RBF kernel,

were determined by dividing the range 2-10-2+10 into 21 steps, resulting in a total of

441 combinations.

Two experiments for the verification of classification performance of the SVM classi-

fier were conducted: cross validation (CV) and independent training and testing (ITT).

For the CV experiment, 5-fold cross-validation was conducted to obtain the optimal

parameters C and g in the training phase. On the other hand, the images dataset was

randomly divided into training set and testing set, each containing 50% of the ran-

domly selected images, for ITT. Again in the training phase, 5-fold cross-validation

was used to obtain the optimal combination of parameters C and g based on the train-

ing set. The ITT experiment were repeated for 10 times.

Table 4 reveals the resulting accuracy obtained from the CV and 10 ITTs with all of

features presented in Table 3. It mimics that the proposed segmentation method is

good enough to detect cell contours for extracting features to design a classifier with

satisfactory classification accuracy. Additionally, one of the objectives of this study is to

select salient features to represent cell patterns.

The sequential backward selection (SBS) [37] was frequently used in many

researches for feature selection. In this study, the SVM-RFE (recursive feature elimi-

nation) reported to be effective for multi-cluster classification [38], was adopted to

eliminate unimportant features according to the minimum redundancy and maxi-

mum relevancy (MRMR) criterion [39]. It was implemented with MIToolbox (Matlab

version) [26]. As shown in Figure 19, the best average accuracy obtained is 99.76%

with 60 features selected for designing the classifier in the CV experiment, while it

achieves 96.90% accuracy for the classifier designed using 124 selected features in the

ITT experiment.

DiscussionCytology evaluation has been shown to be a safe, efficient and well-established techni-

que for the diagnoses of many diseases. Its ability to reduce the mortality and morbid-

ity of cervical cancer through mass screening is the most famous success. Classical

cytological diagnosis is based on microscopic observation of specialized cells and

Table 4 Classification accuracies (%) of different cell patterns

Exp. Iter. All Diffused Peripheral Nucleolar Coarse-speckled Fine-speckled Discr.-speckled

1 - 99.69 98.33 100 98.94 99.95 100 100

2 1 97.65 93.98 99.24 82.98 98.88 85.71 99.33

2 96.45 88.96 98.48 95.74 98.57 71.43 97.65

3 97.23 93.65 97.72 87.23 98.47 78.57 99.66

4 96.45 92.31 98.10 82.98 98.36 78.57 96.64

5 97.13 94.98 96.96 89.36 98.47 71.43 98.66

6 96.81 93.98 99.24 76.60 98.26 85.71 96.98

7 96.81 94.31 97.34 87.23 98.67 82.14 95.64

8 97.33 93.65 98.48 91.49 98.98 78.57 97.32

9 96.50 90.30 98.48 93.62 98.47 85.71 95.97

10 96.50 90.64 98.48 85.10 98.36 82.14 97.65

Avg 96.87 92.68 98.25 87.23 98.55 80.00 97.55

Classified accuracies of 6 patterns of cells and overall cells achieved for CV and ITT experiments.


Page 19 of 25

qualitative assessment with descriptive criteria, which may result in inconsistent results

because of subjective variability found in different observers [40]. Recently, automatic

or semi-automatic computerized systems developed for segmenting and analyzing

stained cervical cells from Pap smear images are demonstrated to be effective and effi-

cient to assist pathologists in the diagnosis of abnormal cells [34,41-43] and in the dis-

crimination of different types of cells [34,44,45] through accurate and objective

measurements of cell texture and morphology.

Tracing the cell migration, cell cycle, and cell differentiation from fluorescent micro-

scopic images through automatic segmentation, classification, and tracking of living

and cultured cells has also been widely conducted [46-48]. However, an automated

image analysis system developed to fit a specific type, assay, or image set is hardly

applicable to different cells acquired from different modalities [49]. Hence, techniques

used for segmenting cells from visible-light microscopic images may not be directly

applied in extracting cells from fluorescent microscopic images, whereas techniques

used for extracting cells in a living cell population from fluorescent microscopic images

may not be effective for processing IIF images.

Tested with the 3830 cells extracted from 196 images, the segmentation results show

that PVO is greater than 89% and PVD is less than 22%. The average classification accu-

racy achieved in this study is as high as 96.90% (error rate: 3.1%) and 99.69% (error rate:

0.31%) for CV and ITT experiments, respectively, which outperforms the performance

reported in previous studies [3,5,6,16-19]. Table 5 compares the cell/image numbers and

the error rates in classification of this study with other previous investigations.

Note that the cells included in the database used in the this study are quite different

from the cells adopted in previous studies, which may induce bias when making com-

parisons. CellProfiler is a freely available software [49] useful for automatic cell

segmentation as well as for quick and easy classification and scoring of cells with

diverse cellular morphologies [48]. Figure 20 compares the segmented cell examples

between the proposed method and the CellProfiler. It can be observed that the

Figure 19 Comparison of accuracy against different number of features. Features selected using SVM-RFE method compared between cross validation (CV) and independent training and testing (ITT)experiments.


Page 20 of 25

Table 5 Comparison of error rate between this study and previous investigations.

Literature Error rate No. of cells/images Validation method

Perner et al. [6] 23.60% NA/321 Human expert

25.00% NA/321 leave-one-out CV

Sack et al. [3] 16.91% 1041/676 CV

Elbischger et al. [17] 9.75% 982/38 ITT (1:1)

Soda & Iannello [5] 5.80% 573/37 8-fold CV

Rigon et al. [16] 26.00% 573/37 8-fold CV

Huang et al. [18] 7.60% 1020/NA 10-fold CV

Voigtet al. [19] 6.00% NA/351 ITT

This study 0.31% 3830/196 5-fold CV

3.10% 3830/196 ITT (1:1)

CV indicates cross validation and ITT denotes independent training and testing.

Figure 20 Comparison of segmentation outcome between proposed method and CellProfiler. (a) Originalimages of 6 different ANA patterns and their segmented results using (b) proposed method and (c) CellProfiler.


Page 21 of 25

Table 6 Comparisons of number of segmented cells and classification performance between proposed method and CellProfiler evaluated based on PVO, PVD,RAE, and MHD.

Pattern Detected Cell Number PVO (%) PVD (%) RAE ± Std. MHD ± Std.

G. Truth Proposed CellProfiler Proposed CellProfiler Proposed CellProfiler Proposed CellProfiler Proposed CellProfiler

Diffuse 251 157 107 87.89 84.75 24.23 30.50 0.6386 ± 0.42 0.5248 ± 0.36 250.90 ± 228.42 510.69 ± 250.56

Peripheral 191 153 97 93.47 72.43 13.06 55.15 0.3039 ± 0.35 0.5120 ± 0.31 87.15 ± 182.86 602.36 ± 267.78

Nucleolar 285 175 101 91.94 84.90 16.12 30.20 0.4777 ± 0.41 0.6445 ± 0.33 157.99 ± 208.70 560.73 ± 225.77

Coarse speckled 363 260 150 92.35 87.84 15.31 24.32 0.3506 ± 0.36 0.7038 ± 0.21 101.90 ± 199.74 556.02 ± 261.51

Fine speckled 117 67 36 91.27 91.22 17.46 17.55 0.5423 ± 0.41 0.8637 ± 0.13 173.20 ± 13.16 458.46 ± 21.41

Discrete speckled 216 195 125 78.03 69.64 43.95 60.71 0.5457 ± 0.37 0.7277 ± 0.18 143.99 ± 202.59 504.55 ± 242.68

Cheng

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2013,12(Suppl1):S5http://w

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proposed method outperforms CellProfiler regarding the individual cells categorized

into 6 different patterns.

In addition to PVO and PVD, other evaluation criteria, including, relative foreground

area error (RAE) [50] and modified Hausdorff distance (MHD) [51], are also used to

measure the segmentation errors. As can be seen in Table 6, the proposed method

demonstrates better segmentation performance over the CellProfiler when evaluated

based on PVO, PVD, RAE, and MHD. In addition, the number of miss-segment cells

of the proposed method is less than the CellProfiler.

Conclusion and future workIn this study, a segmentation method was proposed to detect the boundaries of HEp-2

cells automatically, and then classification of cell patterns was performed based on the

selected features. The results show that the proposed method can detect cells correctly

in most image cases with PVO greater than 89% and PVD less than 22%, whereas the

best combination of selected features can achieve an average accuracy as high as

96.90% in discriminating 6 different types of cell patterns.

More cell images will be included in the dataset for verifying the segmentation

performance and classification performance in the future. Furthermore, an automatic

segmentation and classification system with graphical user interface (GUI) will be

developed for computer-aid diagnosis. In fact, several different ANA patterns can

appear in a single image, but the segmentation method proposed here only considers

images with a unique cell pattern. Future works will focus on developing a segmen-

tation method to extract cells with different patterns appearing in an image.

Competing interestsThe authors declare that they have no competing interests.

Authors’ contributionsCCC designed the software and conducted the image analysis; TYH recruited the patients, acquired the images, andverified the experimental results; CCC, JST, and YFC contributed to the discussion of the work and wrote the paper.All authors read and approved the final manuscript.

AcknowledgementsFunding for this article came from National Science Council of Taiwan under grant NSC100-2410-H-166-007-MY3.This article has been published as part of BioMedical Engineering OnLine Volume 12 Supplement 1, 2013: Selectedarticles from the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society:Workshop on Current Challenging Image Analysis and Information Processing in Life Sciences. The full contents of thesupplement are available online at http://www.biomedical-engineering-online.com/supplement/12/S1

Authors’ details1Department of Electrical Engineering, National Chung Hsing University, Taichung, Republic of China: Taiwan. 2Divisionof Allergy, Immunology and Rheumatology, Taichung Veterans General Hospital, Taichung, Republic of China: Taiwan.3Department of Healthcare Administration, Central Taiwan University of Science and Technology, Taichung, Republicof China: Taiwan. 4Institute of Biomedical Engineering and Material Science, Central Taiwan University of Science andTechnology, Taichung, Republic of China: Taiwan. 5Department of Health Services Administration, China MedicalUniversity, Taichung, Republic of China: Taiwan.

Published: 9 December 2013

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doi:10.1186/1475-925X-12-S1-S5Cite this article as: Cheng et al.: An automatic segmentation and classification framework for anti-nuclearantibody images. BioMedical Engineering OnLine 2013 12(Suppl 1):S5.

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RESEARCH Open Access An automatic segmentation and ... · Anti-Nuclear Antibody (ANA) is an autoantibody produced by the immune system directed against the self body tissues or cells